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Neuron

Article

Normal Movement Selectivity in Autism

Ilan Dinstein,1,* Cibu Thomas,3 Kate Humphreys,3 Nancy Minshew,4 Marlene Behrmann,3 and David J. Heeger1,21

Center for Neural Science2Department of Psychology

New York University, 6 Washington Place, New York, NY 10003, USA3Department of Psychology, Baker Hall, Carnegie Mellon University, Pittsburgh, PA 15213, USA4Department of Neurology, University of Pittsburgh, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA

*Correspondence: [email protected]

DOI 10.1016/j.neuron.2010.03.034

SUMMARY

It has been proposed that individuals with autism

have difficulties understanding the goals and inten-

tions of others because of a fundamental dysfunction

in the mirror neuron system. Here, however, we showthat individuals with autism exhibited not only normal

fMRI responses in mirror system areas during obser-

vation and execution of hand movements but also

exhibited typical movement-selective adaptation

(repetition suppression) when observing or execut-

ing the same movement repeatedly. Movement

selectivity is a defining characteristic of neurons

involved in movement perception, including mirror

neurons, and, as such, these findings argue against

a mirror system dysfunction in autism.

INTRODUCTION

Impaired social interaction is one of the three core symptoms of

the autism spectrum disorders (ASD) (DSM-IV-TR, 2000). This

impairment has been attributed to a dysfunction of the human

mirror neuron system (Fecteau et al., 2006; Iacoboni and

Dapretto, 2006; Oberman and Ramachandran, 2007; Rizzolatti

et al., 2009; Williams et al., 2001 ), which is thought to play

a central role in our ability to perceive the intentions and goals

of others (Rizzolatti and Craighero, 2004). Evidence supporting

this hypothesis comes from neuroimaging studies reporting

weaker mirror system responses in ASD individuals, compared

with typical control individuals, during movement observation,

execution, and imitation tasks. However, previous studies havenot assessed the selectivity of cortical activity in mirror system

areas for particular movements.

Movement selectivity is a fundamental characteristic of

neurons involved in movement perception, including mirror

neurons. Accurate perception and interpretation of an observed

movement requires the ability to distinguish it from other move-

ments by representing it with a unique neural response. Indeed,

movement selectivity is a defining feature of monkey mirror

neurons; different subsets of mirror neurons respond to partic-

ular preferred movements, whether observed or executed, so

that their activity distinguishes between different movements

and also between different intentions/goals (Fogassi et al.,

2005; Gallese et al., 1996; Kohler et al., 2002; Umilta et al.,

2001 ). In previous investigations, we (Dinstein et al., 2007) and

others (Chong et al., 2008; Hamilton and Grafton, 2006; Kilner

et al., 2009; Lingnau et al., 2009; Shmuelof and Zohary, 2005)

have shown that human mirror system areas contain move-

ment-selective neural populations that adapt when hand move-ments are observed and/or executed repeatedly. Do mirror

system areas of individuals with ASD also exhibit such move-

ment-selective responses? The mirror system dysfunction

hypothesis would predict not.

Here, we applied an fMRI adaptation protocol to test whether

high functioning individuals, meeting clinical criteria for autism,

exhibit movement-selective cortical responses equivalent to

those of typical controls. Subjects passively observed images

of hand postures in one experiment and actively executed the

same hand postures in a second experiment. The autism and

control subjects exhibited similarly robust cortical responses

during the observation and execution of hand movements.

Moreover, the profile of movement selectivity in mirror system

areas was equivalent in both groups; anterior intraparietal sulcus

(aIPS) exhibited both motor and visual adaptation (reduced fMRI

responses for repeated versus nonrepeated movements) and

ventral premotor (vPM) cortex exhibited motor adaptation.

These results argue against a mirror system dysfunction in

autism.

RESULTS

The movement observation experiment assessed adaptation in

the visual domain by comparing fMRI (BOLD) responses during

observation of a repeating hand posture with responses during

observation of different hand postures. Similarly, the movement

execution experiment assessed adaptation in the motor domainby comparing fMRI responses during repeated versus non-

repeated execution of hand movements (see Experimental

Procedures). According to the mirror system hypothesis, individ-

uals with autism should exhibit not only weaker mirror system

responses during observation and execution of movements,

but also weaker adaptation during repeated observation and

execution of movements.

Whole-brain SPM analyses of the observation and execution

experiments showed similar results in the autism and control

groups. Typical visual areas responded during movement obser-

vation (Figure 1, green), typical motor system areas responded

during movement execution (Figure 2, blue), and mirror system

Neuron 66, 461469, May 13, 2010 2010 Elsevier Inc. 461
mailto:[email protected]:[email protected]


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areas (aIPS and vPM) responded during both observation and

execution. A direct voxel-by-voxel comparison between the

two subject groups showed no significant differences between

the groups in mirror system areas in either experiment. There

were, however, significantly stronger responses in the control

group in medial visual areas and a left dorsal lateral occipital

area during the movement observation experiment and in an

area below the right aIPS (ipsilateral to the hand used for execu-

tion) during the movement execution experiment (see Figure S1available online).

Figure 1. Cortical Responses during Move-

ment Observation Experiment

Green: Brain areas exhibiting significantlystronger

responses during observation than rest.

Orange: Brain areas exhibiting visual adaptation,

as reflected in significantly stronger responsesduring nonrepeatblocks (when observingdifferent

hand movements) than repeat blocks (same hand

movement repeatedly).

White ellipses outline the general location of the

ROIs, which were selected separately for each

subject.

More importantly, both subject groups

exhibited movement-selective responses

during observation and execution of

movements. The two subject groups ex-

hibited smaller fMRI responses in several

cortical regions including bilateral earlyvisual cortex, lateral occipital cortex,

and anterior intraparietal sulcus, during

blocks where the same movement was

observed repeatedly in comparison to

blocks where different movements were

observed (Figure 1, orange). Both subject groups also exhibited

smaller fMRI responses in several regions including left primary

motor and somatosensory cortex, bilateral cingulate motor

area, bilateral anterior intraparietal sulcus, and left ventral pre-

motor cortex during executed movement repeats versus non-

repeats (Figure 2, orange).

A region of interest (ROI) analysis revealed similar results

across the two groups. We sampled cortical responses from

two commonly reported mirror system areas, aIPS and vPMcortex, as well as from several control areas that are notbelieved

Figure 2. Cortical Responses during Move-

ment Execution Experiment

Blue: Brain areas exhibiting significantly stronger

responses during execution than rest.

Orange: Brain areas exhibiting motor adaptation,

as reflected in significantly stronger responses

during nonrepeatblocks (when executing different

hand movements) than repeat blocks (same hand

movement repeatedly).

White ellipses outline the general location of the

ROIs, which were selected separately for each

subject.

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to contain mirror neurons. These included primary motor and

somatosensory cortex (Mot), early visual cortex (Vis), cingulate

motor area (CMA), and lateral occipital cortex (LO). All regions

of interest were sampled separately from left and right hemi-

sphere except for Mot, which was sampled only in the left hemi-

sphere (movements were executed with the right hand), and

CMA, which was sampled bilaterally due to its medial location

that makes it difficult to separate responses from left and right

hemispheres. These ten ROIs were defined individually for

each of the subjects using a set of functional and anatomical

criteria (see Experimental Procedures, Table S2, and Figure S2).

The ROI sizes were generally smaller in the autism group, but

significantly smaller only in left LO (p < 0.05, two-tailed t test,

uncorrected to increase sensitivity, Figure S2). Note that the sizeof the selected ROIs depended on the statistical significance of

the functional responses, which were a function of response

amplitude and variability. Subjects with greater response vari-

ability would, therefore, be expected to exhibit fewer significantly

activated voxels leading to smaller ROIs despite similar

response amplitudes (see variability results below). The ROIs

were defined based on their overall response to observation

and execution and not based on a comparison between

responses during repeat and nonrepeat blocks. There was,

therefore, no statistical bias for the ROIs to exhibit adaptation.

The fMRI response amplitudes of the autism and control

groups (Figure 3 ) were indistinguishable in the movement

execution and movement observation experiments in all ROIs

(p > 0.05, two-tailed t test, uncorrected to increase sensitivity).

More importantly, both the autism and control groups exhibited

similar magnitudes of adaptation when comparing responses

during movement repeats and nonrepeats in several visual and

motor areas (Figure 3). Significantly smaller visual responses to

repeats were found in left and right LO, left and right aIPS, andin left Vis(p < 0.05, two-tailed paired t test, Bonferroni corrected).

Significantly smaller motor responses to repeats were found in

left Mot, bilateral CMA, left and right aIPS, and left and right

vPM (p < 0.05, two-tailed paired t test, Bonferroni corrected).

Importantly, right and left aIPS exhibited smaller responses to

repeats in both the visual and motor domains. The robustness

of the adaptation effects can be seen in the single subject

adaptation indices, which showed similar response reductions

among individuals of both groups (Figure 4 ) with consistent

visual and motor adaptation in left and right aIPS for the majority

of subjects. The autism group exhibited significantly stronger

visual adaptation in left LO during the observation experiment

Figure 3. Region of Interest Analysis of the Movement Observation

Experiment (Top) and the Movement Execution Experiment (Bottom)

Green: Mean response amplitudes when observing different (nonrepeating)

hand movements, for the autism (light) and control (dark) groups.

Blue: Mean response amplitudes when executing different (nonrepeating)

hand movements, for the autism (light) and control (dark) groups.

Orange: Mean response amplitudes when hand movements were repeated,

for the autism (light) and control (dark) groups.

Error barsrepresent SEM. Asterisks indicatestatistically significant adaptation

(p < 0.05, Bonferroni corrected).

Figure 4. Adaptation Index for Individuals from the Autism Group(Open Squares) and Control Group (Filled Circles)

Top: Visual adaptation index in visual and mirror system ROIs.

Bottom: Motor adaptation index in motor and mirror system ROIs. The index

was computed as the difference between nonrepeat and repeat responses

divided by the absolute nonrepeat response (see Experimental Procedures).

Solid lines denote the average across either the autistic or control subjects.

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and significantly stronger motor adaptation in left Mot during the

execution experiment. All other ROIs, including mirror system

areas, showed no significant difference in the amount of visual

or motor adaptation (p > 0.05, randomization test, see Experi-

mental Procedures and Figure S3 ). Equivalent results were

also found when contracting the selected ROIs to a fixed size

of 100 functional voxels such that ROI size was matched in all

subjects of both groups (Figure S6).

We further characterized the responses of both subject groups

by assessing the variability of the responses in each subject indi-

vidually. Theresponses of a typical autistic subject were less reli-

able/consistent across blocks (strong response to some blocks

and weak response to others) than those of a typical control

subject (error bars in Figure 5 ). To quantify this difference in

response reliability, or, in other words, the within-subject vari-

ability, we performed two complementary analyses. In the first

analysis, we computed the average standard deviation (SD)

across time points and blocks for each subject in each condition(i.e., averaging the error bars of Figure 5, see Experimental

Procedures) and then compared the SDs across individuals of

the two subject groups (Figure 6). Subjects with autism exhibited

significantly larger SDs in right Vis and right vPM during the

movement observation experiment and in left Mot, CMA, left

aIPS, left vPM, and right vPM during the movement execution

experiment (p < 0.05, randomization test, see Experimental

Procedures and Figure S4 ). Larger within-subject variability in

the autism group was equally evident in the responses to both

repeat and nonrepeat blocks.

In a second analysis, we examined how well the GLM of each

experiment fit thefMRI response time courses from each subject

in each ROI. The GLM contained the expected hemodynamic

responses based on the timing of the task blocks and assuming

that the responses to successive blocks of a given condition

were identical. When a particular brain area responds reliably,

there is a good fit between the model and the brain activity

such that a large proportion of the variance in the measured

time-courses can be accounted for by the model. When brain

responses are more noisy in timing or amplitude, the fit with

the GLM is worse. Model fits were worse for individuals with

autism than for controls in several ROIs (Figure 7). Significantly

larger within-subject variability (poorer fit) was evident in left

and right Vis during the movement observation experiment and

in left Mot, left aIPS, and right vPM during the movement execu-

tion experiment (p < 0.05, randomization test, see Experimental

Procedures and Figure S5 ). Importantly, autistic and control

subjects exhibited almost identical hemodynamic responses,

on average (Figure S7). Noisier responses in the autism group

were, therefore, due to variability in the amplitude and timing of

their neural responses and not because of general differences

in the shape or duration of their hemodynamic responses.

DISCUSSION

The results presented here argue against a mirror system

dysfunction in autism. Individuals diagnosed with autism ex-

hibited robust responses in commonly reported mirror system

areas aIPS and vPM both during observation (Figure 1, green)

and execution (Figure 2, blue) of hand movements, which were

equivalent to those of the control group. More importantly,

autistic subjects exhibited visual and motor adaptation in right

Figure 5. Within-Subject Variability

Variance across blocks. Average fMRI responses in left visual areas from

a typical autistic subject (top) and control subject (bottom) during movement

observation blocks. Responses from blocks where different hand movements

were presented (gray) and blocks where the same hand movement was pre-

sented repeatedly (black) were averaged separately. Error bars (SEM across

blocks) are larger for the autistic than the control subject.


Standard deviation across blocks. Average SD across blocks in the movement

observation experiment (top) and movement execution experiment (bottom)

for repeat blocks (white, autism; medium gray, controls) and nonrepeatblocks

(light gray, autism; dark gray, controls). Asterisks indicate significantly larger

SD in the autism group (p < 0.05, randomization test, seeExperimental Proce-

dures). Error bars represent SEM across individuals.

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and left aIPS, which were indistinguishable in magnitude from

those of thecontrol subjects (Figures 3 and 4).We interpretthese

visual and motor adaptation effects as evidence of distinct neural

populations that respond selectively to particular preferred

movements and that adapt (decrease their response) when

a movement is repeatedly observed or executed (Grill-Spector

and Malach, 2001 ). Such responses would be expected from

neural populations that respond selectively to particular move-

ments, including mirror neurons. These experiments, therefore,

targeted a key feature of movement perception not addressed

by previous studiesthe ability of neural populations in mirror

system areas to differentiate between different hand move-

ments. Distinguishing between movements is a critical step for

effectively mapping an observed movement onto the specific

motor neuron population that encodes its execution and for

determining the correct interpretation of the observed personsintentions as hypothesized by mirror system theories (Dinstein,

2008; Dinstein et al., 2008).

In a previous study, we found similar movement-selective

visual and motor adaptation in areas vPM and aIPS of control

subjects (Dinstein et al., 2007). In that study, we asked subjects

to play the rock-paper-scissors game against a videotaped

opponent while freely choosing their executed movement on

eachtrial. We compared repeated versus nonrepeated observed

andexecutedmovements to assessvisual andmotor adaptation

respectively. These adaptation effects were very similar in both

distribution and amplitude to those reported here, albeit using

a different experimental design. This replicability confirms that

visual and motor adaptation is a robust and reproducible

phenomenon across different experimental protocols and

demonstrates its successful use in assessing response selec-

tivity in a population of autistic subjects. Future use of fMRI

adaptation protocols in autism research offers many possibilitiesfor precise characterization of neural population selectivity in

different cortical systems of individuals with autism.

Previous studies that have examined mirror system responses

in autism during observation, execution, and imitation of move-

ments have yielded inconsistent findings. Although some studies

have reported that individuals with autism exhibit weak fMRI

(Dapretto et al., 2006 ), EEG (Martineau et al., 2008; Oberman

et al., 2005), MEG (Nishitani et al., 2004), and TMS-induced cor-

ticospinal excitability (Theoret et al., 2005) responses, other fMRI

(Williams et al., 2006), EEG (Oberman et al., 2008; Raymaekers

et al., 2009), and MEG ( Avikainen et al., 1999) studies have re-

ported that individuals with autism exhibit equivalent responses

to those of controls. There are numerous methodological issues

that could have led to the disparate reports cited above. Forexample, it is difficult to control the behavior of subjects in an

MRI scanner. When subjects are asked to imitate a movement,

delays in the timing or length of the movement may greatly

impact the estimated resulting brain response (e.g., if autistic

subjects always imitate the movement later/slower than

controls, their estimated brain response will seem weaker).

Rather than trying to reconcile the results above, we simply

suggest that the fact that individuals with autism can exhibit

equally strong mirror system responses as those of controls

argues against the claim of a generally dysfunctional mirror

system in autism.

A far stronger argument against a mirror system dysfunction in

autism lies in the finding that individuals with autism exhibit

movement-selective adaptation equivalent to that of controls.

Previous mirror system studies have used several different

experimental tasks to assess mirror system responses, including

passive observation and active imitation protocols using mean-

ingless hand movements, hand-object interactions, symbolic

hand movements, or emotional facial expressions. These

different tasks recruit numerous neural populations (in mirror

system and other cortical areas) that might include mirror

neurons, but also include many other neural populations

involved in vision, motor planning, motor execution, working

memory, and emotion. Mirror neurons make up only about

10% of the neurons that respond during movement observation

or executionin monkeymirror system areas (Fogassi et al., 2005;

Gallese et al., 1996; Kohler et al., 2002; Umilta et al., 2001).Current neuroimaging techniques (fMRI, EEG, and MEG) sum

over the responses of millions of neurons, thereby making it diffi-

cult to discern which of the many overlapping neural populations

generated the responses in the autism and control groups.

Because of this limitation, neither previous mirror system studies

of autism nor the current adaptation study are capable of

isolating the responses of mirror neurons alone. Nevertheless,

by assessing visual andmotoradaptation in mirror systemareas,

we have isolated the responses of movement-selective neural

populations important for movement perception, rather than

summing across the responses of other neural populations

that coexist in these areas (Dinstein, 2008 ). If mirror system


Goodnessof fit.Average goodnessof fitbetweenthe expectedfMRIresponses

and the measured fMRI responses in the movement observation experiment

(top) andmovement execution experiment(bottom)for theautismgroup(white)

and control group (gray). Asterisks indicate significant goodness-of-fit differ-

ence between groups (p < 0.05, randomization test, see Experimental Proce-

dures). Error bars represent SEM.

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theories of movement perception are indeed correct, one would

expect subpopulations of mirror neurons to be tuned to the

movement they encode. This means that mirror system areas

would be expectedto contain circuits of visual, mirror, andmotor

neurons that would be intimately interconnected by their selec-tivity/preference for a particular movement. The fact that these

movement-selective neural circuits respond normally (adapt in

a movement-selective manner) in individuals with autism

suggests that the functional integrity of their mirror system

areas is intact. Characterizing neural selectivity offers a far

more detailed assessment of the mirror systems functional

integrity, which was not possible in previous fMRI studies that

summed over the responses of all neural populations within

these areas.

A further important test of mirror system integrity is cross-

modal adaptation. Cross-modal fMRI adaptation has been

reported in mirrorsystem areas as subjects observe a movement

they have just executed or execute a movement they have just

observed (Chong et al., 2008; Kilner et al., 2009; Lingnau et al.,2009). Such adaptation is a signature of mirror neuron popula-

tions responding repeatedly to their preferred movement regard-

less of whether it is being observed or executed. The current

study was not designed to assess cross-modal adaptation,

although future studies could do so by building on the results

reported here.

In further analyses, we noticed that individual autistic subjects

exhibited larger block-by-block response variability/unreliability

than individual control subjects (Figure 5, error bars). It is well

known that different individuals with autism exhibit distinct and

unique behavioral symptoms. Such behavioral variability may

be expected to generate between-subject cortical response

variability and, indeed, several studies have reported that brain

responses during different motor and visual tasks are more vari-

able across autistic individuals than across control individuals

(Hasson et al., 2009; Humphreys et al., 2008; Muller et al.,

2003; Muller et al., 2001). Here, however, we describe a different

type of variability; variability in the brain responses of single

subjects across different blocks of an experiment. This is a

measure of the consistency or reliability of a single subjects

neural responses across different trials/blocks of the experiment

(within-subject variability). Despite exhibiting equivalent cortical

response amplitudes on average, individuals with autism ex-

hibited significantly larger within-subject variability than controls

in early visual and ventral premotor areas during movement

observation and in several motor areas during movement execu-

tion (Figures 6 and 7). This difference in response variability wasnot due to a general difference in the hemodynamic response,

which was nearly identical in the two groups (Figure S7).

There may be several sources for the greater within-subject

response variability found in the autism group. One possibility is

that individuals with autism behave more variably (with less

consistency) throughout an experiment than control subjects.

For example, subjects with autism may have exhibited noisy

eye movements across blocks, which may have generated

more variable visual system responses during the movement

observation experiment. A more exciting (yet speculative) pos-

sibility is that larger within-subject response variability is a

measure of increased intrinsic neural noise, which may be a

general characteristic of neural networks in autism. Several theo-

ries have proposed that ASD may be caused by early develop-

ment of abnormally connected, noisy, and hyperplastic

cortical networks (Markram et al., 2007; Rubenstein and Merze-

nich,2003) that aremoreprone to epilepsy; a commoncomorbid-ity in autism (Tuchman and Rapin, 2002). These theories suggest

that noisy neural responses may cause the environment to

be perceived as inconsistent and noisy, making it difficult for

the child to cope with the outside world, and driving him/her

to develop autistic behavioral symptoms in response. Further

studies assessing within-subject response variability, while

controlling for within-subject behavioral variability, across age-,

IQ-, and gender-matched subject groups are urgently needed

to investigate this hypothesis.

Regardless of the source of fMRI response variability, our

results clearly show that this variability is not equal across the

two subject groups, as is commonly assumed when interpreting

fMRI studies of autism. An implication of this difference in vari-

ability is that one should exercise caution when comparing acti-vations using statistical parameter maps (SPM) across the two

groups (as done in Figures 1 and 2). Differences in statistical

significance (p values) may be caused by differences in either

the average response amplitude or by differences in the vari-

ability of the response across trials/blocks. For example,

observing a statistically significant activation in the control

group SPM, which is absent in the autism group SPM, might

not be due to a weaker response in the autism group. The

responses might be of equal strength across groups, on

average, but with larger variability in the autism group.

Finally, if the mirror system of ASD individuals responds in

a normal movement-selective manner, why do these individuals

have problems imitating and understanding the movements/

intentions of others? First, it is unclear whether individuals with

autism actually do have such behavioral impairments (Hamilton

et al., 2007). But even if we accept that they do, this question

further assumes that our ability to imitate and understand one

another socially is dependent only on the activity of mirror

neurons. There is little evidence to support such an assumption

(see Dinstein et al., 2008; Hickok, 2009; Southgateand Hamilton,

2008). Even in monkey studies, where mirror neurons have been

successfully isolated (Fogassi et al., 2005; Gallese et al., 1996;

Umilta et al., 2001), there is no evidence for a causal relationship

between mirror neuron activity and the ability of the monkey to

understand the meaning of an observed movement. Proof of

such a relationship would require showing that the removal

(ablation, inactivation) of mirror neurons impairs the monkeysability to understand the meaning of observed movements. As

yet, this experiment has not been performed. There is also no

evidence for a connection between mirror neuron activity and

imitation of movements. This issue has not been studied in

monkeys although there have been reports that macaque

monkeys do imitate, at least during infancy (Ferrari et al.,

2006). Numerous imaging studies have concluded that imitation

and action understanding in humans are abilities that depend on

mirror system responses. However, imaging studies do not test

causality, but rather report brain responses that are associated

with the performance of a particular task. Moreover, these

studies clearly show that activities of numerous visual and motor

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neural populations (not just mirror system areas) are correlated

with imitation and action understanding tasks. There is, there-

fore,no concreteevidence to suggest that a dysfunction in mirror

neurons would cause impairments in imitation or understanding

the intentions of others. Similarly, there is no reason to expectthat individuals with difficulties imitating or understanding

actions necessarily have dysfunctional mirror neurons, rather

than dysfunctions in numerous other neural populations that

play integral roles in these abilities.

EXPERIMENTAL PROCEDURES

Subjects

Thirteen high-functioning male adults with autism (mean age 27.4, range 19 to

40 years old) and ten control subjects (five women and five men, mean age

27.4, range 21 to 35 years old) participated in this study. All subjects had

normal or corrected-to-normal vision, provided written informed consent,

and were paid for their participation in the study. Two control subjects and

two autistic subjects were left-handed, but performed the movements with

their nondominant right hand. The Committee on Activities Involving HumanSubjects at New York University and the Institutional Review Board at Carne-

gie Mellon University and the University of Pittsburgh approved the experi-

mental procedures, which were in compliance with the safety guidelines for

MRI research. For each subject, we obtained a high-resolution anatomical

volume, two runs of the movement observation experiment, and two runs of

the movement execution experiment. Of the data acquired from autistic

subjects, three data sets were excluded because of jerky head movements

exceeding 2 mm. The presented analyses are, therefore, based on data

collected from ten autistic and ten control subjects who completed the exper-

iments successfully.

The diagnosis of autism was established using the Autism Diagnostic Inter-

view Revised (ADI-R) (Lord et al., 1994), the Autism Diagnostic Observation

Schedule (ADOS) (Lord et al., 2000) and expert clinical evaluation (Table S1).

Autistic subjects had an average IQ (intelligence quotient) score of 110 (range

95-128), average ADI social score of 21 (range 15-27), average ADI communi-

cation scoreof 15(range 8-22),averageADIstereotypyscoreof 6 (range3-10),averageADOS social score of 9 (range 5-13), and average ADOS communica-

tion scoreof 5 (range4-6).Potential subjectswith autismwereexcluded if they

had an associated neuropsychiatric or neurological disorder. Exclusion was

based on neurological history and examination, chromosomal analysis, and/

or metabolic testing.

The two subject groups were not matched on IQ or gender. However, this

only increases our confidence in concluding that individuals with autism

exhibited indistinguishable mirror system responses from those of the general

control population.

Visual Stimuli and Motor Response

Stimuli were presented via an LCD projector and custom optics onto a rear-

projection screen in the bore of the MRI scanner. Subjects were supine and

viewed the screen through an angled mirror, which also prevented them

from seeing their own hands. A rectangular foam tray was positioned above

each subjects pelvis and attached to the bed in the scanner. Subjects

executed movements with their hand resting on the upper surface of the

tray. The executed movements were videotaped through a window from the

console room.

Movement Observation Experiment

Subjects passively observed still images of six hand postures (rock, paper,

scissors, thumbs-up, gun, and hang-loose). All subjects completed two runs

of this experiment. Note that still images of movement end-points/postures

rather than video clips of movements have been previously used to assess

mirror system responses in subjects with autism and controls (Dapretto

et al., 2006) and have been reported to elicit robust mirror system responses

in typical subjects (e.g., Aziz-Zadeh et al., 2006; Carr et al., 2003 ). Images

were presented in 9 s blocks of a single hand posture repeated six times

(repeat blocks) or sixdifferenthand postures(nonrepeat blocks).All nonrepeat

blocks contained the same hand postures presented in the same order.

Repeat and nonrepeat blocks were presented in random order, but the

same random order was used in both runs for each subject. Each image

was presented for 750 ms followed by 750 ms of blank and the 9 s of visual

stimulation were followed by 6 s of blank. The experiment contained 9 blocksof each type with 15 s of blank at the beginning and end for a total length of

5 min.

Movement Execution Experiment

Subjects executed the same six hand movements (rock, paper, scissors,

thumbs-up, gun, and hang-loose) using their right hand, as instructed

auditorily. All subjects completed two runs of this experiment. Instructions

were presented in 12 s blocks of a single movementrepeated sixtimes (repeat

blocks) or six different movements (nonrepeat blocks). Repeat and nonrepeat

blocks were arranged randomly, but the same random order was used with all

subjects. Each block contained 12 s of movement execution (2 s for each

movement) followed by 6 s of rest. The experiment contained 10 blocks of

each type with 15 s of rest at the beginning and the end for a total length of

6:30 min. Movement execution trials were slightly longer (2 s) than movement

observation trials (1.5 s) to allow subjects enough time to execute the

movements comfortably.

MRI Acquisition

Functional and anatomical images of the brain were acquired using identical

Siemens (Erlangen, Germany) 3T Allegra MRI scanners located at the NYU

Center for Brain Imaging and the Brain Imaging ResearchCenter in Pittsburgh.

Both scanners wereequippedwith the sameSiemensbirdcage headcoil used

for RF transmit and receive. Blood oxygenation level-dependent (BOLD)

contrast was obtained using a T2*-sensitive echo planar imaging pulse

sequence (repetition time of 1500 ms for movement observation experiment

and 2000 ms for movement execution experiment, echo time = 30 ms, flip

angle = 75, 24 slices, 3 3 3 3 3 mm voxels, field of view = 192 mm). High

resolution anatomical volumes were acquired with a T1-weighted 3D-

MPRAGE pulse sequence (1 3 1 3 1 mm).

Preprocessing, Movement Correction, Segmentation, and Inflation

fMRI data were processed with the Brain Voyager software package

(R. Goebel, Brain Innovation, Maastricht, The Netherlands) and with custom

software written in Matlab (Mathworks, Natick, MA, USA).The firsttwo images

of each functional scan were discarded. Preprocessing of functional scans

included 3D motion correction and temporal high-pass filtering with a cutoff

frequency of 6 cycles per scan. To minimize any residual head movement

artifacts in data sets of both subject groups, after motion correction, the

estimated head motion variables were removed (by orthogonal projection)

from the fMRI time course of each voxel. Functional images were aligned

with the high-resolution anatomical volume using trilinear interpolation, and

the anatomical and functional images were transformed to the Talairach coor-

dinate system (Talairach and Tournoux, 1988). The corticalsurfacewas recon-

structed from the high-resolution anatomical images, separately for each

subject; the procedure included segmenting the gray and white matter and

inflating the gray matter.

Statistical Parameter Mapping

We performed a standard statistical parameter mapping (SPM) analysis

(Friston et al., 1994 ) to assess brain activation associated with each

experimental condition. In short, we constructed a general linear model

(GLM) for the underlying neural response to each experimental condition.

For example, the model for our movement observation experiment was

a matrix that contained a row for each time point, where neural activity was

modeled as either on = 1 or off = 0, and a column for each condition:

repeat and nonrepeat. The expected neural activity model (each column of

the model matrix) was convolved with a canonical hemodynamic impulse

response function to create a model of the expected hemodynamic response

(Boynton et al., 1996). We used linear regression to estimate response ampli-

tudes (beta values) for each voxel and each condition. Response amplitudes

were computed separately for each voxel in each subject and then a paired

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t test was used to determine significant voxel-by-voxel response differences

across conditions (i.e., treating intersubject differences as a random effect)

(Friston et al., 1999). Only voxel clusters exceeding 15 mm3 are displayed in

the statistical maps. Unless stated otherwise, the resulting cortical activation

maps were rendered on a representative individuals cortical surface.

ROI Definition and Analysis

To assess whether cortical areas responding during movement observation

and/or execution exhibited movement-selective adaptation, we defined ten

ROIs individually for each subject using a combination of anatomical and

functional criteria. We overlaid each subjects statistical parameter map for

observation and execution versus rest on their high-resolution anatomical

scan and chose all active voxels within a radius of 15 mm around particular

anatomical landmarks (Figure S2). A false discovery rate (FDR) of 0.05 was

used to threshold the statistical parameter maps of each subject. FDR is

a method of correcting for multiple comparisons by controlling for the

expected proportion of false positives among suprathreshold voxels (Geno-

vese et al., 2002) rather than for the rate of false positives among all voxels

as done by the stricter Bonferroni method. SeeFigure S2 for ROI size compar-

ison and Table S2 for ROI coordinates and a list of the anatomical landmarks

used. Intwo of thecontrolsubjectsand inthreeof theautismsubjects,the FDR

analysisdid notyield anysignificantvoxelsin right andleftvPM.In thesecases

the ROIs were defined entirely based on anatomical criteria, by selecting gray

matter voxels surrounding the junction between the precentral sulcus and the

inferior frontal sulcus.

ROI analyses were carried out by averaging across voxels so as to compute

a single responsetimecourse foreachROI ineachindividual subject. Weused

regression with a GLM to estimate fMRI response amplitudes, as described

above, separatelyfor eachROI in eachsubject individually.We thenperformed

paired t tests to determine which ROIs showed significant response differ-

ences across subjects for selected pairs of conditions (e.g., observed repeat

versus nonrepeat), and corrected for multiple comparisons using the Bonfer-

roni method.

Adaptation Index

Visual and motor adaptation indices werecomputed for eachsubjectand each

ROI separately. The index was the difference between the average nonrepeatresponseand theaveragerepeatresponse divided by theabsolutevalueof the

nonrepeat response: (nonrepeat repeat)/[nonrepeat].

A randomization test was used to assess whether the adaptation indices of

the two groups were statistically different from each other or not. Specifically,

we generated a distribution of index differences, according to the null hypoth-

esis that there was no difference between groups, by randomly assigning indi-

viduals to either subject group (i.e., randomly shuffling subject identities). The

randomization was repeated 10,000 times separately for each ROI to charac-

terize ROI-specific randomized distributions (Figure S3 ). For the adaptation

difference between the autism and control groups in a particular ROI to be

considered statistically significant, it had to fall above the 95th percentile or

below the 5th percentile of the relevant distribution.

We also performedthe sameanalysis using a similaradaptation index where

instead of dividing by the absolute nonrepeat response we divided by the sum

of theabsoluterepeatand nonrepeatresponses.This index hadthe advantage

of being normalized to 1 such that subjects with relatively large or small adap-tation indices had a smaller affect on the mean of the group. This analysis

revealed equivalent results leading to the same conclusions as those reported

using the adaptation index described above.

Within-Subject Response Variability (Trial-Triggered Average)

Response variability was characterized, separately for each experimental

condition, ROI,and subject,by computing the variance across blocks. Specif-

ically, we extracted 15 and 18 s fMRI segments in the visual and motor exper-

iments respectively, which began at the onset of each block. This resulted in

18 visual repeat, 18 visual nonrepeat, 20 motor repeat, and 20 motor nonrep-

eat segments for each ROI and each subject. Figure 5 shows the average

visual repeat and nonrepeat segments taken from left visual cortex of a single

autistic subject and a single control subject during the movement observation

experiment. The error barsin Figure5 represent the standarderror of the mean

(SEM) across blocks for each time point in the segment/block.

To assess variability across subjects, we computed the SD across blocks of

each condition and averaged the SD across all time points in the block (i.e., all

time points plotted in Figure 5). This resulted in a single numerical measure for

each subject; the average SD across blocks of a particular condition. Figure 6showsa comparison of average SDsacrosssubjects of thetwo groups. Statis-

tics were computed using a randomization test similar to that described for

adaptation indices. We generated a distribution of SD differences, by

randomly assigning individuals to either subject group, and determined

whether the difference in SD of the actual two subject groups fell above the

95th percentileor below the5th percentile of the identity-randomized difference

distribution (Figure S4).

Within-Subject Response Variability (Model Fits)

Another method for assessing the response variability was to determine the

goodness of fit between the general linear model and the measured fMRI

response time-courses. Goodness of fit, r, was computed, separately for

each subject and each ROI, as the square root of the variance accounted for

by the estimated hemodynamic responses (Gardner et al., 2005). That is, the

modeled hemodynamic response time course for each condition (repeat or

nonrepeat) was multiplied with the appropriate response amplitude (beta

weight) and the resulting time courses of the two conditions were summed.

The r2 wasthencomputed bydividingthe varianceof themodeledtimecourse

by the variance of the measured time course. Statistics were computed using

a randomizationtest similarto thatdescribedfor adaptation indices(Figure S5).

We generated a distribution of model fit differences, by randomly assigning

individuals to either subjectgroup, anddetermined whetherthe model fit differ-

ence of theactualtwosubjectgroups fell abovethe 95th percentile or below the

5th percentile of the identity-randomized difference distribution.

SUPPLEMENTAL INFORMATION

Supplemental Information includes seven figures and two tables can be found

with this article online at doi:10.1016/j.neuron.2010.03.034.

ACKNOWLEDGMENTS

Supported by National Institutes of Health Grants R01-MH069880 (D.J.H.),

NICHD/NIDCD PO1/U19 (M.B.; PI: Nancy Minshew), F31-MH080457 (I.D.),

Pennsylvania Department of Health grant SAP 4100047862, and Cure Autism

Now (Young Investigator Award, K.H.).

Accepted: March 5, 2010

Published: May 12, 2010

REFERENCES

Avikainen, S., Kulomaki, T., and Hari, R. (1999). Normal movement reading in

Asperger subjects. Neuroreport 10, 34673470.

Aziz-Zadeh, L., Koski, L., Zaidel, E., Mazziotta, J., and Iacoboni, M. (2006).

Lateralization of the human mirror neuron system.J. Neurosci. 26, 29642970.

Boynton, G.M., Engel, S.A., Glover, G.H., and Heeger, D.J. (1996). Linear

systems analysis of functional magnetic resonance imaging in human V1.

J. Neurosci. 16, 42074221.

Carr, L., Iacoboni, M., Dubeau, M.C., Mazziotta, J.C., and Lenzi, G.L. (2003).

Neural mechanisms of empathy in humans: a relay from neural systems for

imitation to limbic areas. Proc. Natl. Acad. Sci. USA 100, 54975502.

Chong, T.T., Cunnington, R., Williams, M.A., Kanwisher, N., and Mattingley,

J.B. (2008). fMRI adaptation reveals mirror neurons in human inferior parietal

cortex. Curr. Biol. 18, 15761580.

Dapretto, M., Davies, M.S., Pfeifer, J.H., Scott, A.A., Sigman, M., Bookheimer,

S.Y., and Iacoboni, M. (2006). Understanding emotions in others: mirror

neuron dysfunction in children with autism spectrum disorders. Nat. Neurosci.

9, 2830.

Neuron


http://dx.doi.org/doi:10.1016/j.neuron.2010.03.034http://dx.doi.org/doi:10.1016/j.neuron.2010.03.034


9/9

Dinstein, I. (2008). Human cortex: reflections of mirror neurons. Curr. Biol. 18,

R956R959.

Dinstein, I., Hasson, U., Rubin, N., and Heeger, D.J. (2007). Brain areas

selective for both observed and executed movements. J. Neurophysiol. 98,

14151427.

Dinstein, I.,Thomas,C., Behrmann,M., andHeeger, D.J. (2008). A mirrorup to

nature. Curr. Biol. 18, R13R18.

DSM-IV-TR. (2000). Diagnostic and Statistical Manual of Mental Disorders

(Washington, DC: American Psychiatric Press Inc.).

Fecteau, S., Lepage, J.F., and Theoret, H. (2006). Autism spectrum disorder:

seeing is not understanding. Curr. Biol. 16, R131R133.

Ferrari,P.F., Visalberghi, E., Paukner,A., Fogassi, L., Ruggiero,A., and Suomi,

S.J. (2006). Neonatal imitation in rhesus macaques. PLoS Biol. 4, e302.

Fogassi, L., Ferrari, P.F., Gesierich, B., Rozzi, S., Chersi, F., and Rizzolatti, G.

(2005). Parietal lobe: from action organization to intention understanding.

Science 308, 662667.

Friston, K.J., Jezzard, P., and Turner, R. (1994). Analysis of functional MRI

time-series. Hum. Brain Mapp. 1, 153171.

Friston, K.J., Holmes, A.P., Price, C.J., Buchel, C., and Worsley, K.J. (1999).

MultisubjectfMRI studiesand conjunction analyses. Neuroimage10, 385396.

Gallese, V., Fadiga, L., Fogassi, L., and Rizzolatti, G. (1996). Action recognition

in the premotor cortex. Brain 119, 593609.

Gardner, J.L., Sun, P., Waggoner, R.A., Ueno, K., Tanaka, K., and Cheng, K.

(2005). Contrast adaptation and representation in human early visual cortex.

Neuron 47, 607620.

Genovese, C.R., Lazar, N.A., and Nichols, T. (2002). Thresholding of statistical

maps in functional neuroimaging using the false discovery rate. Neuroimage

15, 870878.

Grill-Spector, K., and Malach, R. (2001). fMR-adaptation: a tool for studying

the functional properties of human cortical neurons. Acta Psychol. (Amst.)

107, 293321.

Hamilton, A.F., and Grafton, S.T. (2006). Goalrepresentation in humananterior

intraparietal sulcus. J. Neurosci. 26, 11331137.

Hamilton, A.F., Brindley, R.M., and Frith, U. (2007). Imitation and action under-standing in autistic spectrum disorders: how valid is the hypothesis of a deficit

in the mirror neuron system? Neuropsychologia 45, 18591868.

Hasson, U., Avidan, G., Gelbard, H., Vallines, I., Harel, M., Minshew, N., and

Behrmann, M. (2009). Shared and idiosyncratic cortical activation patterns in

autism revealed under continuous real-life viewing conditions. Autism Res.

2, 220231.

Hickok, G. (2009). Eight problems for the mirror neuron theory of action under-

standing in monkeys and humans. J. Cogn. Neurosci. 21, 12291243.

Humphreys, K., Hasson, U., Avidan, G., Minshew, N.J., and Behrmann, M.

(2008). Cortical patterns of category-selective activation for faces, places,

and objects in adults with autism. Autism Res. 1, 5263.

Iacoboni, M., and Dapretto, M. (2006). The mirror neuron system and the

consequences of its dysfunction. Nat. Rev. Neurosci. 7, 942951.

Kilner, J.M., Neal, A., Weiskopf, N., Friston, K.J., and Frith, C.D. (2009).

Evidence of mirror neurons in human inferior frontal gyrus. J. Neurosci. 29,

1015310159.

Kohler, E., Keysers,C., Umilta, M.A., Fogassi, L., Gallese, V., and Rizzolatti, G.

(2002). Hearing sounds, understanding actions:action representation in mirror

neurons. Science 297, 846848.

Lingnau, A., Gesierich, B., and Caramazza, A. (2009). Asymmetric fMRI adap-

tation reveals no evidence for mirror neurons in humans. Proc. Natl. Acad. Sci.

USA106, 99259930.

Lord, C., Rutter, M., and Le Couteur, A. (1994). Autism Diagnostic Interview-

Revised: a revised version of a diagnostic interview for caregivers of individ-

uals with possible pervasive developmental disorders. J. Autism Dev. Disord.

24, 659685.

Lord, C., Risi, S., Lambrecht, L., Cook, E.H., Jr., Leventhal, B.L., DiLavore,

P.C., Pickles, A., and Rutter, M. (2000). The autism diagnostic observation

schedule-generic: a standard measure of social and communication deficits

associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205223.

Markram,H., Rinaldi, T.,and Markram,K. (2007).The intenseworld syndrome- an

alternative hypothesis for autism. Front Neurosci. 1, 7796.

Martineau, J., Cochin, S., Magne, R., and Barthelemy, C. (2008). Impaired

cortical activation in autistic children: is the mirror neuron system involved?

Int. J. Psychophysiol. 68, 3540.

Muller, R.A., Pierce, K., Ambrose, J.B., Allen, G., and Courchesne, E. (2001).

Atypical patterns of cerebral motor activation in autism: a functional magnetic

resonance study. Biol. Psychiatry 49, 665676.

Muller, R.A., Kleinhans, N., Kemmotsu, N., Pierce, K., and Courchesne, E.

(2003). Abnormal variability and distribution of functional maps in autism: an

FMRI study of visuomotor learning. Am. J. Psychiatry 160, 18471862.

Nishitani, N., Avikainen, S., and Hari, R. (2004). Abnormal imitation-related

cortical activation sequences in Aspergers syndrome. Ann. Neurol. 55,

558562.

Oberman, L.M., and Ramachandran, V.S. (2007). The simulating social mind:

the role of the mirror neuron system and simulation in the social and commu-

nicative deficits of autism spectrum disorders. Psychol. Bull. 133, 310327.

Oberman, L.M., Hubbard, E.M., McCleery, J.P., Altschuler, E.L., Ramachan-

dran, V.S., and Pineda, J.A. (2005). EEG evidence for mirror neuron dysfunc-

tion in autism spectrum disorders. Brain Res. Cogn. Brain Res. 24, 190198.

Oberman, L.M., Ramachandran, V.S., and Pineda, J.A. (2008). Modulation of

mu suppression in children with autism spectrum disorders in response to

familiar or unfamiliar stimuli: the mirror neuron hypothesis. Neuropsychologia

46, 15581565.

Raymaekers, R., Wiersema, J.R., and Roeyers, H. (2009). EEG study of the

mirror neuron system in children with high functioning autism. Brain Res.

1304, 113121.

Rizzolatti, G., and Craighero, L. (2004). The mirror-neuron system. Annu. Rev.

Neurosci.27

, 169192.Rizzolatti, G., Fabbri-Destro, M., and Cattaneo, L. (2009). Mirror neurons and

their clinical relevance. Nat. Clin. Pract. Neurol. 5, 2434.

Rubenstein, J.L., and Merzenich, M.M. (2003). Model of autism: increased

ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2,

255267.

Shmuelof, L., and Zohary, E. (2005). Dissociation between ventral and dorsal

fMRI activation during object and action recognition. Neuron 47, 457470.

Southgate, V., and Hamilton, A.F. (2008). Unbroken mirrors: challenging

a theory of Autism. Trends Cogn. Sci. 12, 225229.

Talairach, J., and Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the

Human Brain (New York: Thieme Medical Publishers).

Theoret, H., Halligan, E., Kobayashi, M., Fregni, F., Tager-Flusberg, H., and

Pascual-Leone, A. (2005). Impaired motor facilitation during action observa-

tion in individuals with autism spectrum disorder. Curr. Biol. 15, R84R85.

Tuchman, R., and Rapin, I. (2002). Epilepsy in autism. Lancet Neurol. 1,

352358.

Umilta, M.A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., and

Rizzolatti, G. (2001). I know what you are doing. a neurophysiological study.

Neuron 31, 155165.

Williams, J.H., Whiten, A., Suddendorf, T., and Perrett, D.I. (2001). Imitation,

mirror neurons and autism. Neurosci. Biobehav. Rev. 25, 287295.

Williams, J.H., Waiter, G.D., Gilchrist, A., Perrett, D.I., Murray, A.D., and

Whiten, A. (2006). Neural mechanisms of imitation and mirror neuron func-

tioning in autistic spectrum disorder. Neuropsychologia 44, 610621.

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